Figure S6. Distribution of β ̂ T × B $\hat {\beta }_{T\times B}$ for scenarios with K=36, β k = β v , and low censoring (A) or high censoring (B) for no biomarker–treatment interaction (βT×B= ln(1.0)=0, top rows) or qualitative biomarker–treatment interaction (βT×B= ln(1.33)=0.285, bottom rows). Results for different correlation structures are shown in separate columns. The dashed red lines indicate the true value of βT×B, the blue triangles represent the observed confidence interval coverages, the green dots the observed probability for a type I error (A) or estimated power (B). (PDF 20 kb
The bias in estimates of(βAge) (A),(βSex) (B), βε4 (C), βG (D), and βG×ε4 (E) obtained using the usu...
Tables A1-A3. Impact of α0 and ω on the operating characteristics for scenarios S1 to S12. These tab...
Text S1. Set-based model invalidation and parameter estimation. Text S2. Mathematical modelling. Tex...
Figure S5. Distribution of β ̂ T × B $\hat {\beta }_{T\times B}$ for scenarios with K=36, β k = β eq...
Figure S1. Distribution of β ̂ T × B $\hat {\beta }_{T\times B}$ for scenarios with K=12, β k = β eq...
Figure S3. Distribution of β ̂ T × B $\hat {\beta }_{T\times B}$ for scenarios with K=24, β k = β eq...
Table S3. Mean number of additionally included prognostic variables for all scenarios with K=36. (PD...
Table S1. Mean number of additionally included prognostic variables for all scenarios with K=12. (PD...
Table S2. Mean number of additionally included prognostic variables for all scenarios with K=24. (PD...
Figure S1. Binary Trait Type I Errors. The plot shows the Type I errors for different parameter sett...
Accuracy and precision of the survival probabilities, and coverage probability of the associated 95%...
Additional file 1: Supplementary Figure 1. Data simulation pipeline. Simulation approach is an integ...
Table S1. TPR, FPR and FDR in variable selection with 50 replications (Weibull distribution). Table ...
Figure S6. Power estimates for the individual and adaptive tests. The censoring scheme, Ci ~ Unif(0,...
Estimation of biomarkers’ median effect. It provides supplementary tables of the application results...
The bias in estimates of(βAge) (A),(βSex) (B), βε4 (C), βG (D), and βG×ε4 (E) obtained using the usu...
Tables A1-A3. Impact of α0 and ω on the operating characteristics for scenarios S1 to S12. These tab...
Text S1. Set-based model invalidation and parameter estimation. Text S2. Mathematical modelling. Tex...
Figure S5. Distribution of β ̂ T × B $\hat {\beta }_{T\times B}$ for scenarios with K=36, β k = β eq...
Figure S1. Distribution of β ̂ T × B $\hat {\beta }_{T\times B}$ for scenarios with K=12, β k = β eq...
Figure S3. Distribution of β ̂ T × B $\hat {\beta }_{T\times B}$ for scenarios with K=24, β k = β eq...
Table S3. Mean number of additionally included prognostic variables for all scenarios with K=36. (PD...
Table S1. Mean number of additionally included prognostic variables for all scenarios with K=12. (PD...
Table S2. Mean number of additionally included prognostic variables for all scenarios with K=24. (PD...
Figure S1. Binary Trait Type I Errors. The plot shows the Type I errors for different parameter sett...
Accuracy and precision of the survival probabilities, and coverage probability of the associated 95%...
Additional file 1: Supplementary Figure 1. Data simulation pipeline. Simulation approach is an integ...
Table S1. TPR, FPR and FDR in variable selection with 50 replications (Weibull distribution). Table ...
Figure S6. Power estimates for the individual and adaptive tests. The censoring scheme, Ci ~ Unif(0,...
Estimation of biomarkers’ median effect. It provides supplementary tables of the application results...
The bias in estimates of(βAge) (A),(βSex) (B), βε4 (C), βG (D), and βG×ε4 (E) obtained using the usu...
Tables A1-A3. Impact of α0 and ω on the operating characteristics for scenarios S1 to S12. These tab...
Text S1. Set-based model invalidation and parameter estimation. Text S2. Mathematical modelling. Tex...